{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- [字典转换datafram](https://github.com/vi3k6i5/pandas_basics/blob/master/1_a_create_a_dataframe_from_dictonary.ipynb)\n",
    "- [pandas处理大数据量级](https://www.jianshu.com/p/c862130f322d)\n",
    "- [嵌套json](https://blog.csdn.net/qq_17550379/article/details/80276477)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- https://codeday.me/bug/20180916/251775.html\n",
    "- https://www.kaggle.com/jboysen/quick-tutorial-flatten-nested-json-in-pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  100 101 102 103 104 66 67 68 69 70 ... 90 91 92 93 94 95 96 97 98 99\n",
      "0   A   D   Y   Y   C  N  R  P  S  G ...  T  I  S  G  L  Q  A  E  D  E\n",
      "\n",
      "[1 rows x 36 columns]\n",
      "  100 101 102 103 104 66 67A 68 69 70 ... 90 91 92 93 94 95 96 97 98 99\n",
      "0   A   D   Y   Y   C  S   R  M  S  G ...  T  I  S  G  L  Q  A  E  D  E\n",
      "\n",
      "[1 rows x 36 columns]\n",
      "  100 101 102 103 104 66 67 68 69 70 ... 90 91 92 93 94 95 96 97 98 99\n",
      "0   A   D   Y   Y   C  S  R  P  S  G ...  T  I  S  G  L  Q  A  E  D  E\n",
      "\n",
      "[1 rows x 36 columns]\n",
      "  100 101 102 103 104 66 67 68 69 70 ... 90 91 92 93 94 95 96 97B 98 99\n",
      "0   A   D   Y   Y   C  S  R  P  S  G ...  T  I  S  G  L  Q  A   E  D  E\n",
      "\n",
      "[1 rows x 36 columns]\n",
      "  100 101 102 103 104 66 67 68 69 70 ... 90 91 92 93 94 95 96 97 98 99\n",
      "0   A   D   Y   Y   C  V  G  P  Q  G ...  T  I  S  G  L  Q  A  E  D  E\n",
      "\n",
      "[1 rows x 36 columns]\n"
     ]
    }
   ],
   "source": [
    "import json\n",
    "from pandas.io.json import json_normalize\n",
    "import pandas as pd\n",
    "jsonfile = r'small.json'\n",
    "with open(jsonfile, 'r') as infile:\n",
    "    for line in infile:\n",
    "        uline = json.loads(line)\n",
    "        df = json_normalize(data=uline,)\n",
    "        print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>100</th>\n",
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       "      <th>103</th>\n",
       "      <th>104</th>\n",
       "      <th>66</th>\n",
       "      <th>67</th>\n",
       "      <th>67A</th>\n",
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       "      <th>69</th>\n",
       "      <th>...</th>\n",
       "      <th>91</th>\n",
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       "      <th>94</th>\n",
       "      <th>95</th>\n",
       "      <th>96</th>\n",
       "      <th>97</th>\n",
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       "      <th>1</th>\n",
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       "      <td>Y</td>\n",
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       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>C</td>\n",
       "      <td>S</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
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       "      <td>...</td>\n",
       "      <td>I</td>\n",
       "      <td>S</td>\n",
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       "      <td>L</td>\n",
       "      <td>Q</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>C</td>\n",
       "      <td>S</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>S</td>\n",
       "      <td>...</td>\n",
       "      <td>I</td>\n",
       "      <td>S</td>\n",
       "      <td>G</td>\n",
       "      <td>L</td>\n",
       "      <td>Q</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>C</td>\n",
       "      <td>V</td>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>Q</td>\n",
       "      <td>...</td>\n",
       "      <td>I</td>\n",
       "      <td>S</td>\n",
       "      <td>G</td>\n",
       "      <td>L</td>\n",
       "      <td>Q</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  100 101 102 103 104 66   67  67A 68 69 ... 91 92 93 94 95 96   97  97B 98 99\n",
       "0   A   D   Y   Y   C  N    R  NaN  P  S ...  I  S  G  L  Q  A    E  NaN  D  E\n",
       "1   A   D   Y   Y   C  S  NaN    R  M  S ...  I  S  G  L  Q  A    E  NaN  D  E\n",
       "2   A   D   Y   Y   C  S    R  NaN  P  S ...  I  S  G  L  Q  A    E  NaN  D  E\n",
       "3   A   D   Y   Y   C  S    R  NaN  P  S ...  I  S  G  L  Q  A  NaN    E  D  E\n",
       "4   A   D   Y   Y   C  V    G  NaN  P  Q ...  I  S  G  L  Q  A    E  NaN  D  E\n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = []\n",
    "with open(jsonfile, 'r') as infile:\n",
    "    for line in infile:\n",
    "        uline = json.loads(line)\n",
    "        data.append(uline)\n",
    "df = json_normalize(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: '67A'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-13-5a0f4dd5455a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf_sort\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdf_sort\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-13-5a0f4dd5455a>\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf_sort\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdf_sort\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: '67A'"
     ]
    }
   ],
   "source": [
    "df_sort = df.reindex(sorted(df.columns, key=lambda x: float(x)), axis=1) \n",
    "df_sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "_nsre = re.compile('([0-9]+)')\n",
    "def natural_sort_key(s):\n",
    "    return [int(text) if text.isdigit() else text.lower()\n",
    "            for text in re.split(_nsre, s)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sort = df.reindex(sorted(df.columns, key=natural_sort_key), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>66</th>\n",
       "      <th>67</th>\n",
       "      <th>67A</th>\n",
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       "      <th>69</th>\n",
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       "      <th>71</th>\n",
       "      <th>72</th>\n",
       "      <th>74</th>\n",
       "      <th>75</th>\n",
       "      <th>...</th>\n",
       "      <th>96</th>\n",
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       "      <th>97B</th>\n",
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       "      <td>...</td>\n",
       "      <td>A</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
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       "      <th>1</th>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>R</td>\n",
       "      <td>M</td>\n",
       "      <td>S</td>\n",
       "      <td>G</td>\n",
       "      <td>V</td>\n",
       "      <td>S</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>S</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>S</td>\n",
       "      <td>G</td>\n",
       "      <td>V</td>\n",
       "      <td>S</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>S</td>\n",
       "      <td>R</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>S</td>\n",
       "      <td>G</td>\n",
       "      <td>V</td>\n",
       "      <td>S</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>NaN</td>\n",
       "      <td>E</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>V</td>\n",
       "      <td>G</td>\n",
       "      <td>NaN</td>\n",
       "      <td>P</td>\n",
       "      <td>Q</td>\n",
       "      <td>G</td>\n",
       "      <td>F</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>R</td>\n",
       "      <td>...</td>\n",
       "      <td>A</td>\n",
       "      <td>E</td>\n",
       "      <td>NaN</td>\n",
       "      <td>D</td>\n",
       "      <td>E</td>\n",
       "      <td>A</td>\n",
       "      <td>D</td>\n",
       "      <td>Y</td>\n",
       "      <td>Y</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  66   67  67A 68 69 70 71 72 74 75 ... 96   97  97B 98 99 100 101 102 103 104\n",
       "0  N    R  NaN  P  S  G  V  S  N  R ...  A    E  NaN  D  E   A   D   Y   Y   C\n",
       "1  S  NaN    R  M  S  G  V  S  R  R ...  A    E  NaN  D  E   A   D   Y   Y   C\n",
       "2  S    R  NaN  P  S  G  V  S  R  R ...  A    E  NaN  D  E   A   D   Y   Y   C\n",
       "3  S    R  NaN  P  S  G  V  S  R  R ...  A  NaN    E  D  E   A   D   Y   Y   C\n",
       "4  V    G  NaN  P  Q  G  F  R  R  R ...  A    E  NaN  D  E   A   D   Y   Y   C\n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    0.6\n",
       "V    0.2\n",
       "N    0.2\n",
       "Name: 66, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['66'].value_counts(normalize=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>T</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V</th>\n",
       "      <td>0.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.8</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>18 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    66    67  67A   68   69   70   71   72   74   75 ...    96   97  97B   98  \\\n",
       "A  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   1.0  NaN  NaN  NaN   \n",
       "C  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN  NaN  NaN   \n",
       "D  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN  NaN  1.0   \n",
       "E  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  1.0  1.0  NaN   \n",
       "F  NaN   NaN  NaN  NaN  NaN  NaN  0.2  NaN  NaN  NaN ...   NaN  NaN  NaN  NaN   \n",
       "G  NaN  0.25  NaN  NaN  NaN  1.0  NaN  NaN  NaN  NaN ...   NaN  NaN  NaN  NaN   \n",
       "I  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN  NaN  NaN   \n",
       "K  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN  NaN  NaN   \n",
       "L  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN  NaN  NaN   \n",
       "M  NaN   NaN  NaN  0.2  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN  NaN  NaN   \n",
       "N  0.2   NaN  NaN  NaN  NaN  NaN  NaN  NaN  0.2  NaN ...   NaN  NaN  NaN  NaN   \n",
       "P  NaN   NaN  NaN  0.8  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN  NaN  NaN   \n",
       "Q  NaN   NaN  NaN  NaN  0.2  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN  NaN  NaN   \n",
       "R  NaN  0.75  1.0  NaN  NaN  NaN  NaN  0.2  0.8  1.0 ...   NaN  NaN  NaN  NaN   \n",
       "S  0.6   NaN  NaN  NaN  0.8  NaN  NaN  0.8  NaN  NaN ...   NaN  NaN  NaN  NaN   \n",
       "T  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN  NaN  NaN   \n",
       "V  0.2   NaN  NaN  NaN  NaN  NaN  0.8  NaN  NaN  NaN ...   NaN  NaN  NaN  NaN   \n",
       "Y  NaN   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN ...   NaN  NaN  NaN  NaN   \n",
       "\n",
       "    99  100  101  102  103  104  \n",
       "A  NaN  1.0  NaN  NaN  NaN  NaN  \n",
       "C  NaN  NaN  NaN  NaN  NaN  1.0  \n",
       "D  NaN  NaN  1.0  NaN  NaN  NaN  \n",
       "E  1.0  NaN  NaN  NaN  NaN  NaN  \n",
       "F  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "G  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "I  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "K  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "L  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "M  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "N  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "P  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "Q  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "R  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "S  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "T  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "V  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "Y  NaN  NaN  NaN  1.0  1.0  NaN  \n",
       "\n",
       "[18 rows x 39 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sort.apply(lambda x: pd.value_counts(x, normalize=True),axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "ww_df=df_sort.apply(lambda x: pd.value_counts(x, normalize=True),axis=0)\n",
    "ff_df = ww_df.fillna(0).T\n",
    "ff_df.to_csv('mytest.csv',index_label='pos')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logomaker\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r'D:/WORKcode/VL-naive-NGS-data/IGLV2-14.cdrl2.csv')\n",
    "ww_df = df.drop(['pos'],axis =1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x180 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ww_logo = logomaker.Logo(ww_df,\n",
    "                         font_name='Arial',\n",
    "                         color_scheme='NajafabadiEtAl2017',\n",
    "                         vpad=.1,\n",
    "                         width=.8)\n",
    "\n",
    "# style using Logo methods\n",
    "ww_logo.style_xticks(anchor=0, spacing=5)\n",
    "\n",
    "\n",
    "# style using Axes methods\n",
    "\n",
    "ww_logo.ax.set_xlim([-1,len(ww_df)])\n",
    "#ww_logo.ax.set_xticklabels(range(56,66))\n",
    "ww_logo.ax.set_xticks([])\n",
    "ww_logo.ax.set_yticks([])\n",
    "ww_logo.ax.set_title('Title')\n",
    "plt.savefig('foo.png', dpi=600)\n",
    "ww_logo.fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
